A vehicle is typically classified into one of the six (6) classes: sedans, pickups, minivans, SUVs (Sports Utility Vehicles), buses and trucks. Currently, it is believed that no such system exists for classifying on-road civilian vehicles. State of the art video based vehicle classifiers classify the vehicles based on their size alone. Consequently, these systems fail in fine grain classification of various passenger and commercial vehicles.
Academic research has been done for side views of the vehicle; however, prior work for classifying vehicle from rear view video does not exist. A typical system has a camera pointed at a road lane such that the horizontal scan lines of the camera are parallel to the horizon. Accordingly, it would be desirable to have a software system for hierarchical classification of vehicles from a video of the rear view and/or back end of the vehicle.
In addition, current methods and systems of passenger vehicle classification rely on side views of the vehicles. Generally, the side profile of the vehicle is the key information used to accurately classify the vehicles. However, on a multi-lane road, the side profiles can be easily occluded by other vehicles. Additionally, most of the cameras deployed along the highways and other road capture rear views of the vehicle making the side profile based techniques useless. Thus, there is a need for a system and method that classifies passenger vehicles based on a camera image of the back end or rear of the vehicle.